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<<<<<<< HEAD
=======
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(#fig:edu_gender)Education level count by gender
## `summarise()` has grouped output by 'SA4_CODE_2016'. You can override using the `.groups` argument.
Figure 1: Population distribution of education level
Figure 2: Best education level of each region
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Figure 3: Population distribution of field
Figure 4: Best field of each region
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## ℹ Use `spec()` for the full column specifications.## `summarise()` has grouped output by 'SA4_CODE_2016'. You can override using the `.groups` argument.## Joining, by = "SA4_CODE_2016"
| SA4_CODE_2016 | femalepopulation | malepopulation | population |
|---|---|---|---|
| 201 | 32726 | 34691 | 67417 |
| 202 | 32396 | 34054 | 66450 |
| 203 | 60660 | 64307 | 124967 |
| 204 | 35934 | 39614 | 75548 |
| 205 | 52929 | 57572 | 110501 |
| 206 | 159362 | 160819 | 320181 |
| 207 | 81814 | 86786 | 168600 |
| 208 | 96482 | 101671 | 198153 |
| 209 | 109370 | 122195 | 231565 |
| 210 | 71224 | 85167 | 156391 |
| 211 | 118179 | 129501 | 247680 |
| 212 | 151481 | 184164 | 335645 |
| 213 | 147830 | 178340 | 326170 |
| 214 | 62731 | 68190 | 130921 |
| 215 | 29867 | 33492 | 63359 |
| 216 | 25915 | 28796 | 54711 |
| 217 | 26236 | 29297 | 55533 |
| 297 | 0 | 9 | 9 |
| 299 | 765 | 1229 | 1994 |
Highest people are are Health Care Professionals and the ratio between men to women is less than one.
Similarly, in construction more men are employed as labourers.
The population of women in the education sector is far exceeds that of men.
Management & Commerce is the field that the most population have studied.
More men have studied Engineering and Technology as compared to females. However, more people are employed in Health Care than in industries relating to Engineering.
More women have studied Management and Commerce, however more men are employed as managers.
Victorian population is educated upto level 7 and most are employed as professionals.
However, a large population is employed as labourers when the population share of people who studied below high school is very less.
[GenderLinearModel] shows the relationship between male and female populations
Most of the residents achieved the level 7, which refers to the bachelor degree, and there are almost twice as many female as male.
Majority of male residents achieved at the level 3 and 4.
## `summarise()` has grouped output by 'educationlevel'. You can override using the `.groups` argument.## Joining, by = c("node", "category")
## `summarise()` has grouped output by 'industry'. You can override using the `.groups` argument.## Joining, by = c("node", "category")
## `summarise()` has grouped output by 'field'. You can override using the `.groups` argument.## Joining, by = c("node", "category")
<<<<<<< HEAD Figure 1: Spatial Industry Distribution ======= Figure 5: Spatial Industry Distribution >>>>>>> fc57fe83b5555588ff478f7a4d72bf7679cd7c6a
## `summarise()` has grouped output by 'SA4_CODE_2016'. You can override using the `.groups` argument.
## `summarise()` has grouped output by 'occupation'. You can override using the `.groups` argument.## Joining, by = c("node", "category")
## `summarise()` has grouped output by 'afq_level'. You can override using the `.groups` argument.| afq_level | age_min | population |
|---|---|---|
| Level 1 & 2 | 15 | 9402 |
| Level 3 & 4 | 25 | 146297 |
| Level 5 & 6 | 25 | 96920 |
| Level 7 | 25 | 245613 |
| Level 9 | 25 | 83204 |
| Not Stated | 25 | 70455 |
| Level 8 | 35 | 28908 |
## `summarise()` has grouped output by 'industry'. You can override using the `.groups` argument.| industry | age_min | population |
|---|---|---|
| Accommodation_and_food_services | 25 | 42103 |
| Administrative_and_support_services | 25 | 23086 |
| Arts_and_recreation_services | 25 | 13149 |
| Construction | 25 | 61959 |
| Electricity_gas_water_and_waste_service | 25 | 8039 |
| Financial_and_insurance_services | 25 | 32021 |
| Health_care_and_social_assistance | 25 | 80994 |
| Information_media_and_telecommunications | 25 | 14702 |
| Not Stated | 25 | 29901 |
| Other_services | 25 | 24089 |
| Professional_scientific_and_technical_services | 25 | 64125 |
| Rental_hiring_and_real_estate_services | 25 | 11796 |
| Retail_trade | 25 | 61803 |
| Mining | 35 | 2441 |
| Wholesale_trade | 35 | 22199 |
| Education_and_training | 45 | 56125 |
| Manufacturing | 45 | 55206 |
| Public_administration_and_safety | 45 | 37747 |
| Transport_postal_and_warehousing | 45 | 32663 |
| Agriculture_forestry_and_fishing | 55 | 12733 |
## `summarise()` has grouped output by 'field'. You can override using the `.groups` argument.| field | age_min | population |
|---|---|---|
| Mixed_Field_Programmes | 15 | 1813 |
| Architecture_and_Building | 25 | 42510 |
| Creative_Arts | 25 | 40334 |
| Food_Hospitality_and_Personal_Services | 25 | 42938 |
| Health | 25 | 67630 |
| Information_Technology | 25 | 37535 |
| Management_and_Commerce | 25 | 150571 |
| Natural_and_Physical_Sciences | 25 | 22171 |
| Not Stated | 25 | 71440 |
| Society_and_Culture | 25 | 80932 |
| Agriculture_Environment | 35 | 13016 |
| Engineering_and_Technologies | 45 | 77524 |
| Education | 55 | 44696 |
| NA | NA | 896 |
## `summarise()` has grouped output by 'occupation'. You can override using the `.groups` argument.| occupation | age_min | population |
|---|---|---|
| Community_and_personal_service_workers | 25 | 67104 |
| Not Stated | 25 | 11075 |
| Professionals | 25 | 190449 |
| Sales_workers | 25 | 51772 |
| Technicians_and_trades_workers | 25 | 99110 |
| Managers | 35 | 100601 |
| Clerical_and_administrative_workers | 45 | 89021 |
| Labourers | 45 | 49653 |
| Machinery_operators_and_drivers | 45 | 40922 |
The bar plots represent the SA4 regions and its working population with respect to their education levels, field of study, industry of employment and occupations.
It can be observed that the region 206 had the most number of people with highest education levels which justifies that highest number of people in region 2016 were employed as professionals in their respective industries.
Management and commerce, engineering and technology were the fields of study for most population and agriculture, environment and mixed field programs had the least population share.
Health care, manufacturing and retail trade were the industries with most population while people were employed most for occupations of Professionals and Managers.
## `summarise()` has grouped output by 'SA4_CODE_2016'. You can override using the `.groups` argument.Figure 1: Best education level of each region
## `summarise()` has grouped output by 'SA4_CODE_2016'. You can override using the `.groups` argument.
## `summarise()` has grouped output by 'SA4_CODE_2016'. You can override using the `.groups` argument.Figure 2: Best field of each region
## `summarise()` has grouped output by 'SA4_CODE_2016'. You can override using the `.groups` argument.
The maps represent the SA4 regions and the distribution of population by their education levels, industries, field of study and occupations respectively.
Most population has completed education level 7 with management and commerce as their respective fields of study.
It can be observed that the highest number of people are employed in the occupations: Professionals, Managers and Technicians and trade workers.
Major industry in the city side is healthcare and the country regions are more operational in agricultural activities.
## `summarise()` has grouped output by 'SA4_CODE_2016'. You can override using the `.groups` argument.
Figure 3: Spatial Education Level Distribution
## `summarise()` has grouped output by 'SA4_CODE_2016'. You can override using the `.groups` argument.
Figure 4: Spatial Industry Distribution
## `summarise()` has grouped output by 'SA4_CODE_2016'. You can override using the `.groups` argument.
<<<<<<< HEAD <<<<<<< HEAD Figure 2: Spatial Industry Distribution ======= Figure 6: Spatial Industry Distribution >>>>>>> fc57fe83b5555588ff478f7a4d72bf7679cd7c6a ======= Figure 5: Spatial Study Field Distribution >>>>>>> de6ccec71946224bd62212ac6377ff2c1d5cfdfc
## `summarise()` has grouped output by 'SA4_CODE_2016'. You can override using the `.groups` argument.
Figure 6: Spatial Occupation Distribution
## `summarise()` has grouped output by 'SA4_CODE_2016'. You can override using the `.groups` argument.## Joining, by = c("node", "category")
## `summarise()` has grouped output by 'SA4_CODE_2016'. You can override using the `.groups` argument.## Joining, by = c("node", "category")
## `summarise()` has grouped output by 'SA4_CODE_2016'. You can override using the `.groups` argument.## Joining, by = c("node", "category")
## `summarise()` has grouped output by 'SA4_CODE_2016'. You can override using the `.groups` argument.## Joining, by = c("node", "category")
## `summarise()` has grouped output by 'SA4_CODE_2016'. You can override using the `.groups` argument.
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## hr_max
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It can be observed from fig @ref(fig:hr_plots) that overall females worked more than men. However, as the number of work-hours increased men have worked more than women.
It can be observed from fig @ref(fig:ind_hrs) that industries like health care, education and training, construction and Professional and technical services have more working population as the working hours increased. Mining, electricity, gas, water and agriculture forestry and fishing showed low working population irrespective of work hours.
## # A tibble: 1 x 1
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It can be observed from fig @ref(fig:hrs_plots) that overall females worked more than men at all occupations. Although, for maximum hours worked, as number of working-hours increased, the number of men and women remained the same.
## `summarise()` has grouped output by 'SA4_CODE_2016'. You can override using the `.groups` argument.
It can be observed from fig ?? tha the most number of employees in the SA4 regions are employed in the occupations of Professionals, Managers and Technicians and trade workers. Professionals accounted for highest number of employees for region 206, while machinery operators and drivers accounted for the least number of employees for region 213 respectively.
>>>>>>> fc57fe83b5555588ff478f7a4d72bf7679cd7c6a =======
Conclusion
The education levels, field of study, industry of employment and occupation was studied for the Victorian SA4 level populations for the distributions according to gender and sex. The tables and plots were compared to mark the covariations between the population distributions.For example, the population trend between the field of study and industry of employment. Networks were drawn based on the population weights to analyze these trends. Some of the trends like more men were employed as managers when more women had studied management were found to be interesting. Cholropeth maps were made to analyze these trends spatially.
The goal of this report is to create a data story from these statistical summaries to enumerate the facts from the data and link them to the real world. The data provided by the Australian Bureau of Statistics is an aggregated open data and in no form identifies individuals who participated in the census. The ABS aims to integrate the census data with other datasets to make this census data more interesting. Thus, we aim to do the same and bring some interesting data stories as we progress building this report.
R Core Team (2021)
Xie (2021a) Dietrich (2020) Wickham et al. (2021),
Wickham (2021a),
Wickham et al. (2020),
Zhu (2021),
Xie (2021b),
Tierney et al. (2020),
Pedersen (2020),
Henry and Wickham (2020),
Wickham and Hester (2020),
Wickham and Seidel (2020),
Wickham (2019),
Müller and Wickham (2021),
Wickham (2021b),
Wickham (2021c),
Xie (2021c),
Tierney (2019),
Xie (2016),
Wickham (2016),
Xie (2015),
Xie (2014),
Wickham et al. (2019),
Xie (2019),
Tierney (2017)